Overview

Dataset statistics

Number of variables26
Number of observations203
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory41.4 KiB
Average record size in memory208.7 B

Variable types

Numeric15
Categorical11

Alerts

modele has a high cardinality: 140 distinct valuesHigh cardinality
niveau_risque_assurance is highly overall correlated with empattement(cm) and 2 other fieldsHigh correlation
empattement(cm) is highly overall correlated with niveau_risque_assurance and 11 other fieldsHigh correlation
longueur_voiture(cm) is highly overall correlated with empattement(cm) and 9 other fieldsHigh correlation
largeur_voiture(cm) is highly overall correlated with empattement(cm) and 10 other fieldsHigh correlation
hauteur_voiture(cm) is highly overall correlated with niveau_risque_assurance and 3 other fieldsHigh correlation
poids_vehicule(kg) is highly overall correlated with empattement(cm) and 8 other fieldsHigh correlation
taille_moteur is highly overall correlated with empattement(cm) and 12 other fieldsHigh correlation
taux_alésage(cm) is highly overall correlated with empattement(cm) and 9 other fieldsHigh correlation
course_piston(cm) is highly overall correlated with emplacement_moteur and 1 other fieldsHigh correlation
taux_compression is highly overall correlated with carburant and 3 other fieldsHigh correlation
chevaux is highly overall correlated with empattement(cm) and 11 other fieldsHigh correlation
tour_moteur is highly overall correlated with carburantHigh correlation
consommation_ville(L/100km) is highly overall correlated with longueur_voiture(cm) and 7 other fieldsHigh correlation
consommation_autoroute(L/100km) is highly overall correlated with empattement(cm) and 9 other fieldsHigh correlation
prix is highly overall correlated with empattement(cm) and 8 other fieldsHigh correlation
carburant is highly overall correlated with taux_compression and 2 other fieldsHigh correlation
turbo is highly overall correlated with taux_compression and 1 other fieldsHigh correlation
nombre_portes is highly overall correlated with niveau_risque_assurance and 2 other fieldsHigh correlation
type_vehicule is highly overall correlated with nombre_portesHigh correlation
roues_motrices is highly overall correlated with marqueHigh correlation
emplacement_moteur is highly overall correlated with empattement(cm) and 4 other fieldsHigh correlation
type_moteur is highly overall correlated with taille_moteur and 3 other fieldsHigh correlation
nombre_cylindres is highly overall correlated with largeur_voiture(cm) and 6 other fieldsHigh correlation
systeme_carburant is highly overall correlated with taux_compression and 3 other fieldsHigh correlation
marque is highly overall correlated with empattement(cm) and 9 other fieldsHigh correlation
carburant is highly imbalanced (53.6%)Imbalance
emplacement_moteur is highly imbalanced (88.9%)Imbalance
nombre_cylindres is highly imbalanced (57.3%)Imbalance
modele is uniformly distributedUniform
niveau_risque_assurance has 66 (32.5%) zerosZeros

Reproduction

Analysis started2023-04-28 08:56:22.094311
Analysis finished2023-04-28 08:56:55.594371
Duration33.5 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

niveau_risque_assurance
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.83251232
Minimum-2
Maximum3
Zeros66
Zeros (%)32.5%
Negative25
Negative (%)12.3%
Memory size1.7 KiB
2023-04-28T10:56:55.709299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-1
Q10
median1
Q32
95-th percentile3
Maximum3
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2473842
Coefficient of variation (CV)1.4983373
Kurtosis-0.67225171
Mean0.83251232
Median Absolute Deviation (MAD)1
Skewness0.2135015
Sum169
Variance1.5559674
MonotonicityNot monotonic
2023-04-28T10:56:55.853742image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 66
32.5%
1 54
26.6%
2 31
15.3%
3 27
13.3%
-1 22
 
10.8%
-2 3
 
1.5%
ValueCountFrequency (%)
-2 3
 
1.5%
-1 22
 
10.8%
0 66
32.5%
1 54
26.6%
2 31
15.3%
3 27
13.3%
ValueCountFrequency (%)
3 27
13.3%
2 31
15.3%
1 54
26.6%
0 66
32.5%
-1 22
 
10.8%
-2 3
 
1.5%

carburant
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
gas
183 
diesel
20 

Length

Max length6
Median length3
Mean length3.2955665
Min length3

Characters and Unicode

Total characters669
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowgas
2nd rowgas
3rd rowgas
4th rowgas
5th rowgas

Common Values

ValueCountFrequency (%)
gas 183
90.1%
diesel 20
 
9.9%

Length

2023-04-28T10:56:56.040594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-28T10:56:56.236516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
gas 183
90.1%
diesel 20
 
9.9%

Most occurring characters

ValueCountFrequency (%)
s 203
30.3%
g 183
27.4%
a 183
27.4%
e 40
 
6.0%
d 20
 
3.0%
i 20
 
3.0%
l 20
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 669
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 203
30.3%
g 183
27.4%
a 183
27.4%
e 40
 
6.0%
d 20
 
3.0%
i 20
 
3.0%
l 20
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 669
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 203
30.3%
g 183
27.4%
a 183
27.4%
e 40
 
6.0%
d 20
 
3.0%
i 20
 
3.0%
l 20
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 669
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 203
30.3%
g 183
27.4%
a 183
27.4%
e 40
 
6.0%
d 20
 
3.0%
i 20
 
3.0%
l 20
 
3.0%

turbo
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
std
166 
turbo
37 

Length

Max length5
Median length3
Mean length3.364532
Min length3

Characters and Unicode

Total characters683
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowstd
2nd rowstd
3rd rowstd
4th rowstd
5th rowstd

Common Values

ValueCountFrequency (%)
std 166
81.8%
turbo 37
 
18.2%

Length

2023-04-28T10:56:56.567101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-28T10:56:56.747770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
std 166
81.8%
turbo 37
 
18.2%

Most occurring characters

ValueCountFrequency (%)
t 203
29.7%
s 166
24.3%
d 166
24.3%
u 37
 
5.4%
r 37
 
5.4%
b 37
 
5.4%
o 37
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 683
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 203
29.7%
s 166
24.3%
d 166
24.3%
u 37
 
5.4%
r 37
 
5.4%
b 37
 
5.4%
o 37
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 683
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 203
29.7%
s 166
24.3%
d 166
24.3%
u 37
 
5.4%
r 37
 
5.4%
b 37
 
5.4%
o 37
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 683
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 203
29.7%
s 166
24.3%
d 166
24.3%
u 37
 
5.4%
r 37
 
5.4%
b 37
 
5.4%
o 37
 
5.4%

nombre_portes
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
four
114 
two
89 

Length

Max length4
Median length4
Mean length3.5615764
Min length3

Characters and Unicode

Total characters723
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtwo
2nd rowtwo
3rd rowtwo
4th rowfour
5th rowfour

Common Values

ValueCountFrequency (%)
four 114
56.2%
two 89
43.8%

Length

2023-04-28T10:56:56.884337image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-28T10:56:57.048244image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
four 114
56.2%
two 89
43.8%

Most occurring characters

ValueCountFrequency (%)
o 203
28.1%
f 114
15.8%
u 114
15.8%
r 114
15.8%
t 89
12.3%
w 89
12.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 723
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 203
28.1%
f 114
15.8%
u 114
15.8%
r 114
15.8%
t 89
12.3%
w 89
12.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 723
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 203
28.1%
f 114
15.8%
u 114
15.8%
r 114
15.8%
t 89
12.3%
w 89
12.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 723
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 203
28.1%
f 114
15.8%
u 114
15.8%
r 114
15.8%
t 89
12.3%
w 89
12.3%

type_vehicule
Categorical

Distinct5
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
berline
95 
hayon
69 
break
25 
coupé
 
8
décapotable
 
6

Length

Max length11
Median length5
Mean length6.1133005
Min length5

Characters and Unicode

Total characters1241
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdécapotable
2nd rowdécapotable
3rd rowhayon
4th rowberline
5th rowberline

Common Values

ValueCountFrequency (%)
berline 95
46.8%
hayon 69
34.0%
break 25
 
12.3%
coupé 8
 
3.9%
décapotable 6
 
3.0%

Length

2023-04-28T10:56:57.184548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-28T10:56:57.370140image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
berline 95
46.8%
hayon 69
34.0%
break 25
 
12.3%
coupé 8
 
3.9%
décapotable 6
 
3.0%

Most occurring characters

ValueCountFrequency (%)
e 221
17.8%
n 164
13.2%
b 126
10.2%
r 120
9.7%
a 106
8.5%
l 101
8.1%
i 95
7.7%
o 83
 
6.7%
y 69
 
5.6%
h 69
 
5.6%
Other values (7) 87
 
7.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1241
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 221
17.8%
n 164
13.2%
b 126
10.2%
r 120
9.7%
a 106
8.5%
l 101
8.1%
i 95
7.7%
o 83
 
6.7%
y 69
 
5.6%
h 69
 
5.6%
Other values (7) 87
 
7.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1241
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 221
17.8%
n 164
13.2%
b 126
10.2%
r 120
9.7%
a 106
8.5%
l 101
8.1%
i 95
7.7%
o 83
 
6.7%
y 69
 
5.6%
h 69
 
5.6%
Other values (7) 87
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1227
98.9%
None 14
 
1.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 221
18.0%
n 164
13.4%
b 126
10.3%
r 120
9.8%
a 106
8.6%
l 101
8.2%
i 95
7.7%
o 83
 
6.8%
y 69
 
5.6%
h 69
 
5.6%
Other values (6) 73
 
5.9%
None
ValueCountFrequency (%)
é 14
100.0%

roues_motrices
Categorical

Distinct3
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
traction
118 
propulsion
76 
quatre_roues_motrices
 
9

Length

Max length21
Median length8
Mean length9.3251232
Min length8

Characters and Unicode

Total characters1893
Distinct characters15
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowpropulsion
2nd rowpropulsion
3rd rowpropulsion
4th rowtraction
5th rowquatre_roues_motrices

Common Values

ValueCountFrequency (%)
traction 118
58.1%
propulsion 76
37.4%
quatre_roues_motrices 9
 
4.4%

Length

2023-04-28T10:56:57.561336image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-28T10:56:57.726378image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
traction 118
58.1%
propulsion 76
37.4%
quatre_roues_motrices 9
 
4.4%

Most occurring characters

ValueCountFrequency (%)
o 288
15.2%
t 254
13.4%
r 221
11.7%
i 203
10.7%
n 194
10.2%
p 152
8.0%
a 127
6.7%
c 127
6.7%
u 94
 
5.0%
s 94
 
5.0%
Other values (5) 139
7.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1875
99.0%
Connector Punctuation 18
 
1.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 288
15.4%
t 254
13.5%
r 221
11.8%
i 203
10.8%
n 194
10.3%
p 152
8.1%
a 127
6.8%
c 127
6.8%
u 94
 
5.0%
s 94
 
5.0%
Other values (4) 121
6.5%
Connector Punctuation
ValueCountFrequency (%)
_ 18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1875
99.0%
Common 18
 
1.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 288
15.4%
t 254
13.5%
r 221
11.8%
i 203
10.8%
n 194
10.3%
p 152
8.1%
a 127
6.8%
c 127
6.8%
u 94
 
5.0%
s 94
 
5.0%
Other values (4) 121
6.5%
Common
ValueCountFrequency (%)
_ 18
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1893
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 288
15.2%
t 254
13.4%
r 221
11.7%
i 203
10.7%
n 194
10.2%
p 152
8.0%
a 127
6.7%
c 127
6.7%
u 94
 
5.0%
s 94
 
5.0%
Other values (5) 139
7.3%

emplacement_moteur
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
avant
200 
arriere
 
3

Length

Max length7
Median length5
Mean length5.0295567
Min length5

Characters and Unicode

Total characters1021
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowavant
2nd rowavant
3rd rowavant
4th rowavant
5th rowavant

Common Values

ValueCountFrequency (%)
avant 200
98.5%
arriere 3
 
1.5%

Length

2023-04-28T10:56:57.873944image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-28T10:56:58.041477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
avant 200
98.5%
arriere 3
 
1.5%

Most occurring characters

ValueCountFrequency (%)
a 403
39.5%
v 200
19.6%
n 200
19.6%
t 200
19.6%
r 9
 
0.9%
e 6
 
0.6%
i 3
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1021
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 403
39.5%
v 200
19.6%
n 200
19.6%
t 200
19.6%
r 9
 
0.9%
e 6
 
0.6%
i 3
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 1021
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 403
39.5%
v 200
19.6%
n 200
19.6%
t 200
19.6%
r 9
 
0.9%
e 6
 
0.6%
i 3
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1021
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 403
39.5%
v 200
19.6%
n 200
19.6%
t 200
19.6%
r 9
 
0.9%
e 6
 
0.6%
i 3
 
0.3%

empattement(cm)
Real number (ℝ)

Distinct53
Distinct (%)26.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean250.92478
Minimum219.96
Maximum307.09
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-28T10:56:58.190593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum219.96
5-th percentile236.245
Q1240.03
median246.38
Q3260.1
95-th percentile279.4
Maximum307.09
Range87.13
Interquartile range (IQR)20.07

Descriptive statistics

Standard deviation15.341711
Coefficient of variation (CV)0.061140676
Kurtosis0.98325987
Mean250.92478
Median Absolute Deviation (MAD)6.86
Skewness1.0377435
Sum50937.73
Variance235.36809
MonotonicityNot monotonic
2023-04-28T10:56:58.369062image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
240.03 21
 
10.3%
238 19
 
9.4%
243.08 13
 
6.4%
245.11 8
 
3.9%
249.94 7
 
3.4%
247.14 7
 
3.4%
250.95 6
 
3.0%
244.6 6
 
3.0%
251.71 6
 
3.0%
274.07 6
 
3.0%
Other values (43) 104
51.2%
ValueCountFrequency (%)
219.96 2
 
1.0%
224.54 1
 
0.5%
225.04 2
 
1.0%
227.33 3
 
1.5%
231.9 2
 
1.0%
236.22 1
 
0.5%
236.47 5
 
2.5%
236.98 1
 
0.5%
238 19
9.4%
239.52 1
 
0.5%
ValueCountFrequency (%)
307.09 1
 
0.5%
293.62 2
 
1.0%
290.07 4
2.0%
287.02 2
 
1.0%
284.48 1
 
0.5%
279.4 3
1.5%
277.11 5
2.5%
274.32 1
 
0.5%
274.07 6
3.0%
271.02 1
 
0.5%

longueur_voiture(cm)
Real number (ℝ)

Distinct74
Distinct (%)36.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean442.32507
Minimum358.39
Maximum528.57
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-28T10:56:58.571942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum358.39
5-th percentile399.54
Q1423.035
median439.93
Q3465.58
95-th percentile499.567
Maximum528.57
Range170.18
Interquartile range (IQR)42.545

Descriptive statistics

Standard deviation31.339007
Coefficient of variation (CV)0.070850624
Kurtosis-0.077959593
Mean442.32507
Median Absolute Deviation (MAD)17.53
Skewness0.14764244
Sum89791.99
Variance982.13338
MonotonicityNot monotonic
2023-04-28T10:56:58.763749image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
399.54 15
 
7.4%
479.55 11
 
5.4%
436.12 7
 
3.4%
474.22 7
 
3.4%
422.4 7
 
3.4%
419.86 6
 
3.0%
451.61 6
 
3.0%
447.55 6
 
3.0%
473.96 6
 
3.0%
449.07 5
 
2.5%
Other values (64) 127
62.6%
ValueCountFrequency (%)
358.39 1
 
0.5%
367.28 2
 
1.0%
381 3
 
1.5%
395.99 3
 
1.5%
399.03 1
 
0.5%
399.54 15
7.4%
401.07 1
 
0.5%
403.1 3
 
1.5%
403.35 1
 
0.5%
404.11 3
 
1.5%
ValueCountFrequency (%)
528.57 1
 
0.5%
514.6 2
1.0%
506.98 2
1.0%
505.97 1
 
0.5%
505.21 4
2.0%
500.38 1
 
0.5%
492.25 1
 
0.5%
489.46 3
1.5%
486.92 1
 
0.5%
484.89 2
1.0%

largeur_voiture(cm)
Real number (ℝ)

Distinct43
Distinct (%)21.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean167.44399
Minimum153.16
Maximum183.64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-28T10:56:58.954497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum153.16
5-th percentile161.54
Q1162.81
median166.37
Q3169.93
95-th percentile179.019
Maximum183.64
Range30.48
Interquartile range (IQR)7.12

Descriptive statistics

Standard deviation5.4572944
Coefficient of variation (CV)0.03259176
Kurtosis0.68330679
Mean167.44399
Median Absolute Deviation (MAD)3.56
Skewness0.8958297
Sum33991.13
Variance29.782062
MonotonicityNot monotonic
2023-04-28T10:56:59.128642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
162.05 24
 
11.8%
168.91 23
 
11.3%
166.12 14
 
6.9%
161.54 11
 
5.4%
173.74 10
 
4.9%
163.58 10
 
4.9%
162.56 9
 
4.4%
166.37 8
 
3.9%
165.61 7
 
3.4%
166.62 6
 
3.0%
Other values (33) 81
39.9%
ValueCountFrequency (%)
153.16 1
 
0.5%
156.97 1
 
0.5%
158.75 1
 
0.5%
161.54 11
5.4%
162.05 24
11.8%
162.31 3
 
1.5%
162.56 9
 
4.4%
162.81 2
 
1.0%
163.07 6
 
3.0%
163.58 10
4.9%
ValueCountFrequency (%)
183.64 1
 
0.5%
182.88 1
 
0.5%
182.12 3
1.5%
181.36 3
1.5%
180.09 1
 
0.5%
179.32 1
 
0.5%
179.07 1
 
0.5%
178.56 3
1.5%
176.78 2
1.0%
175.01 4
2.0%

hauteur_voiture(cm)
Real number (ℝ)

Distinct49
Distinct (%)24.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean136.47621
Minimum121.41
Maximum151.89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-28T10:56:59.327109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum121.41
5-th percentile126.24
Q1132.08
median137.41
Q3140.97
95-th percentile146.05
Maximum151.89
Range30.48
Interquartile range (IQR)8.89

Descriptive statistics

Standard deviation6.2337263
Coefficient of variation (CV)0.045676286
Kurtosis-0.46410539
Mean136.47621
Median Absolute Deviation (MAD)4.07
Skewness0.055909592
Sum27704.67
Variance38.859343
MonotonicityNot monotonic
2023-04-28T10:56:59.509903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
129.03 14
 
6.9%
141.48 12
 
5.9%
132.08 12
 
5.9%
138.43 10
 
4.9%
137.41 10
 
4.9%
140.97 9
 
4.4%
137.92 8
 
3.9%
144.02 8
 
3.9%
142.49 7
 
3.4%
133.6 7
 
3.4%
Other values (39) 106
52.2%
ValueCountFrequency (%)
121.41 1
 
0.5%
123.95 2
 
1.0%
125.48 2
 
1.0%
125.98 4
 
2.0%
126.24 3
 
1.5%
127.51 6
3.0%
128.27 2
 
1.0%
128.52 5
 
2.5%
129.03 14
6.9%
129.54 1
 
0.5%
ValueCountFrequency (%)
151.89 2
 
1.0%
150.11 3
 
1.5%
149.1 4
2.0%
148.08 1
 
0.5%
146.05 3
 
1.5%
144.02 8
3.9%
143.51 2
 
1.0%
143 2
 
1.0%
142.75 3
 
1.5%
142.49 7
3.4%

poids_vehicule(kg)
Real number (ℝ)

Distinct170
Distinct (%)83.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1161.2325
Minimum674.95
Maximum1844.31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-28T10:56:59.701268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum674.95
5-th percentile862.056
Q1988.605
median1097.69
Q31335.145
95-th percentile1589.387
Maximum1844.31
Range1169.36
Interquartile range (IQR)346.54

Descriptive statistics

Standard deviation236.42183
Coefficient of variation (CV)0.20359561
Kurtosis-0.055750087
Mean1161.2325
Median Absolute Deviation (MAD)177.36
Skewness0.66684908
Sum235730.19
Variance55895.281
MonotonicityNot monotonic
2023-04-28T10:56:59.884585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1081.82 4
 
2.0%
902.2 3
 
1.5%
869.99 3
 
1.5%
1031.92 3
 
1.5%
1155.75 2
 
1.0%
993.82 2
 
1.0%
1149.86 2
 
1.0%
918.07 2
 
1.0%
1094.97 2
 
1.0%
1844.31 2
 
1.0%
Other values (160) 178
87.7%
ValueCountFrequency (%)
674.95 1
0.5%
777 1
0.5%
825.08 1
0.5%
833.25 1
0.5%
850.03 2
1.0%
850.94 2
1.0%
856.84 1
0.5%
857.29 1
0.5%
861.83 1
0.5%
864.09 1
0.5%
ValueCountFrequency (%)
1844.31 2
1.0%
1791.69 1
0.5%
1769.01 1
0.5%
1710.04 1
0.5%
1700.97 1
0.5%
1696.44 1
0.5%
1685.1 1
0.5%
1671.49 1
0.5%
1594.38 1
0.5%
1589.84 1
0.5%

type_moteur
Categorical

Distinct7
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
ohc
148 
ohcv
 
13
ohcf
 
13
dohc
 
12
l
 
12
Other values (2)
 
5

Length

Max length5
Median length3
Mean length3.1182266
Min length1

Characters and Unicode

Total characters633
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st rowdohc
2nd rowdohc
3rd rowohcv
4th rowohc
5th rowohc

Common Values

ValueCountFrequency (%)
ohc 148
72.9%
ohcv 13
 
6.4%
ohcf 13
 
6.4%
dohc 12
 
5.9%
l 12
 
5.9%
rotor 4
 
2.0%
dohcv 1
 
0.5%

Length

2023-04-28T10:57:00.059797image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-28T10:57:00.252419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
ohc 148
72.9%
ohcv 13
 
6.4%
ohcf 13
 
6.4%
dohc 12
 
5.9%
l 12
 
5.9%
rotor 4
 
2.0%
dohcv 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
o 195
30.8%
h 187
29.5%
c 187
29.5%
v 14
 
2.2%
f 13
 
2.1%
d 13
 
2.1%
l 12
 
1.9%
r 8
 
1.3%
t 4
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 633
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 195
30.8%
h 187
29.5%
c 187
29.5%
v 14
 
2.2%
f 13
 
2.1%
d 13
 
2.1%
l 12
 
1.9%
r 8
 
1.3%
t 4
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 633
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 195
30.8%
h 187
29.5%
c 187
29.5%
v 14
 
2.2%
f 13
 
2.1%
d 13
 
2.1%
l 12
 
1.9%
r 8
 
1.3%
t 4
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 633
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 195
30.8%
h 187
29.5%
c 187
29.5%
v 14
 
2.2%
f 13
 
2.1%
d 13
 
2.1%
l 12
 
1.9%
r 8
 
1.3%
t 4
 
0.6%

nombre_cylindres
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct7
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
four
157 
six
24 
five
 
11
eight
 
5
two
 
4
Other values (2)
 
2

Length

Max length6
Median length4
Mean length3.9014778
Min length3

Characters and Unicode

Total characters792
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st rowfour
2nd rowfour
3rd rowsix
4th rowfour
5th rowfive

Common Values

ValueCountFrequency (%)
four 157
77.3%
six 24
 
11.8%
five 11
 
5.4%
eight 5
 
2.5%
two 4
 
2.0%
three 1
 
0.5%
twelve 1
 
0.5%

Length

2023-04-28T10:57:00.421228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-28T10:57:00.639010image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
four 157
77.3%
six 24
 
11.8%
five 11
 
5.4%
eight 5
 
2.5%
two 4
 
2.0%
three 1
 
0.5%
twelve 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
f 168
21.2%
o 161
20.3%
r 158
19.9%
u 157
19.8%
i 40
 
5.1%
s 24
 
3.0%
x 24
 
3.0%
e 20
 
2.5%
v 12
 
1.5%
t 11
 
1.4%
Other values (4) 17
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 792
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
f 168
21.2%
o 161
20.3%
r 158
19.9%
u 157
19.8%
i 40
 
5.1%
s 24
 
3.0%
x 24
 
3.0%
e 20
 
2.5%
v 12
 
1.5%
t 11
 
1.4%
Other values (4) 17
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 792
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
f 168
21.2%
o 161
20.3%
r 158
19.9%
u 157
19.8%
i 40
 
5.1%
s 24
 
3.0%
x 24
 
3.0%
e 20
 
2.5%
v 12
 
1.5%
t 11
 
1.4%
Other values (4) 17
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 792
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
f 168
21.2%
o 161
20.3%
r 158
19.9%
u 157
19.8%
i 40
 
5.1%
s 24
 
3.0%
x 24
 
3.0%
e 20
 
2.5%
v 12
 
1.5%
t 11
 
1.4%
Other values (4) 17
 
2.1%

taille_moteur
Real number (ℝ)

Distinct44
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.14778
Minimum61
Maximum326
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-28T10:57:00.815579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum61
5-th percentile90
Q197.5
median120
Q3143
95-th percentile202.1
Maximum326
Range265
Interquartile range (IQR)45.5

Descriptive statistics

Standard deviation41.773527
Coefficient of variation (CV)0.3285431
Kurtosis5.2380141
Mean127.14778
Median Absolute Deviation (MAD)23
Skewness1.9335616
Sum25811
Variance1745.0276
MonotonicityNot monotonic
2023-04-28T10:57:00.990230image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
122 15
 
7.4%
92 15
 
7.4%
98 14
 
6.9%
97 13
 
6.4%
90 12
 
5.9%
108 12
 
5.9%
110 12
 
5.9%
109 8
 
3.9%
120 7
 
3.4%
141 7
 
3.4%
Other values (34) 88
43.3%
ValueCountFrequency (%)
61 1
 
0.5%
70 3
 
1.5%
79 1
 
0.5%
80 1
 
0.5%
90 12
5.9%
91 5
 
2.5%
92 15
7.4%
97 13
6.4%
98 14
6.9%
103 1
 
0.5%
ValueCountFrequency (%)
326 1
 
0.5%
308 1
 
0.5%
304 1
 
0.5%
258 2
 
1.0%
234 2
 
1.0%
209 3
1.5%
203 1
 
0.5%
194 3
1.5%
183 4
2.0%
181 6
3.0%
Distinct8
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
mpfi
94 
2bbl
64 
idi
20 
1bbl
11 
spdi
 
9
Other values (3)
 
5

Length

Max length4
Median length4
Mean length3.8965517
Min length3

Characters and Unicode

Total characters791
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st rowmpfi
2nd rowmpfi
3rd rowmpfi
4th rowmpfi
5th rowmpfi

Common Values

ValueCountFrequency (%)
mpfi 94
46.3%
2bbl 64
31.5%
idi 20
 
9.9%
1bbl 11
 
5.4%
spdi 9
 
4.4%
4bbl 3
 
1.5%
mfi 1
 
0.5%
spfi 1
 
0.5%

Length

2023-04-28T10:57:01.156046image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-28T10:57:01.345472image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
mpfi 94
46.3%
2bbl 64
31.5%
idi 20
 
9.9%
1bbl 11
 
5.4%
spdi 9
 
4.4%
4bbl 3
 
1.5%
mfi 1
 
0.5%
spfi 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
b 156
19.7%
i 145
18.3%
p 104
13.1%
f 96
12.1%
m 95
12.0%
l 78
9.9%
2 64
8.1%
d 29
 
3.7%
1 11
 
1.4%
s 10
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 713
90.1%
Decimal Number 78
 
9.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
b 156
21.9%
i 145
20.3%
p 104
14.6%
f 96
13.5%
m 95
13.3%
l 78
10.9%
d 29
 
4.1%
s 10
 
1.4%
Decimal Number
ValueCountFrequency (%)
2 64
82.1%
1 11
 
14.1%
4 3
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 713
90.1%
Common 78
 
9.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
b 156
21.9%
i 145
20.3%
p 104
14.6%
f 96
13.5%
m 95
13.3%
l 78
10.9%
d 29
 
4.1%
s 10
 
1.4%
Common
ValueCountFrequency (%)
2 64
82.1%
1 11
 
14.1%
4 3
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 791
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
b 156
19.7%
i 145
18.3%
p 104
13.1%
f 96
12.1%
m 95
12.0%
l 78
9.9%
2 64
8.1%
d 29
 
3.7%
1 11
 
1.4%
s 10
 
1.3%

taux_alésage(cm)
Real number (ℝ)

Distinct38
Distinct (%)18.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.4497044
Minimum6.45
Maximum10.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-28T10:57:01.525215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum6.45
5-th percentile7.54
Q18
median8.41
Q39.09
95-th percentile9.6
Maximum10.01
Range3.56
Interquartile range (IQR)1.09

Descriptive statistics

Standard deviation0.68679836
Coefficient of variation (CV)0.081280756
Kurtosis-0.76222615
Mean8.4497044
Median Absolute Deviation (MAD)0.59
Skewness0.039531047
Sum1715.29
Variance0.47169199
MonotonicityNot monotonic
2023-04-28T10:57:01.688214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
9.19 21
 
10.3%
8.1 20
 
9.9%
8 15
 
7.4%
7.7 12
 
5.9%
7.54 12
 
5.9%
8.79 9
 
4.4%
8.41 8
 
3.9%
8.71 8
 
3.9%
9.6 8
 
3.9%
8.31 7
 
3.4%
Other values (28) 83
40.9%
ValueCountFrequency (%)
6.45 1
 
0.5%
6.81 1
 
0.5%
7.39 7
3.4%
7.42 1
 
0.5%
7.54 12
5.9%
7.59 1
 
0.5%
7.65 5
2.5%
7.7 12
5.9%
7.75 6
3.0%
7.82 1
 
0.5%
ValueCountFrequency (%)
10.01 2
 
1.0%
9.65 2
 
1.0%
9.6 8
 
3.9%
9.55 1
 
0.5%
9.5 3
 
1.5%
9.4 5
 
2.5%
9.22 2
 
1.0%
9.19 21
10.3%
9.17 1
 
0.5%
9.14 1
 
0.5%

course_piston(cm)
Real number (ℝ)

Distinct36
Distinct (%)17.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.2883251
Minimum5.26
Maximum10.59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-28T10:57:01.858354image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum5.26
5-th percentile6.71
Q17.9
median8.36
Q38.66
95-th percentile9.25
Maximum10.59
Range5.33
Interquartile range (IQR)0.76

Descriptive statistics

Standard deviation0.77577183
Coefficient of variation (CV)0.093598142
Kurtosis2.4066613
Mean8.2883251
Median Absolute Deviation (MAD)0.36
Skewness-0.66027981
Sum1682.53
Variance0.60182193
MonotonicityNot monotonic
2023-04-28T10:57:02.020771image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
8.64 20
 
9.9%
8 14
 
6.9%
7.7 14
 
6.9%
8.2 14
 
6.9%
8.61 13
 
6.4%
6.71 10
 
4.9%
8.51 9
 
4.4%
8.36 9
 
4.4%
8.79 8
 
3.9%
7.8 6
 
3.0%
Other values (26) 86
42.4%
ValueCountFrequency (%)
5.26 1
 
0.5%
5.56 2
 
1.0%
6.71 10
4.9%
6.81 2
 
1.0%
7.01 1
 
0.5%
7.11 2
 
1.0%
7.29 1
 
0.5%
7.37 3
 
1.5%
7.7 14
6.9%
7.8 6
3.0%
ValueCountFrequency (%)
10.59 2
 
1.0%
9.91 3
 
1.5%
9.8 4
2.0%
9.25 5
2.5%
9.09 6
3.0%
8.99 4
2.0%
8.94 5
2.5%
8.89 6
3.0%
8.81 4
2.0%
8.79 8
3.9%

taux_compression
Real number (ℝ)

Distinct32
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.15133
Minimum7
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-28T10:57:02.180454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile7.5
Q18.55
median9
Q39.4
95-th percentile21.86
Maximum23
Range16
Interquartile range (IQR)0.85

Descriptive statistics

Standard deviation3.9905801
Coefficient of variation (CV)0.39310909
Kurtosis5.1392945
Mean10.15133
Median Absolute Deviation (MAD)0.4
Skewness2.5938477
Sum2060.72
Variance15.924729
MonotonicityNot monotonic
2023-04-28T10:57:02.326224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
9 45
22.2%
9.4 26
12.8%
8.5 14
 
6.9%
9.5 12
 
5.9%
9.3 11
 
5.4%
8.7 9
 
4.4%
8 8
 
3.9%
9.2 8
 
3.9%
7 7
 
3.4%
8.6 5
 
2.5%
Other values (22) 58
28.6%
ValueCountFrequency (%)
7 7
3.4%
7.5 5
 
2.5%
7.6 4
 
2.0%
7.7 2
 
1.0%
7.8 1
 
0.5%
8 8
3.9%
8.1 2
 
1.0%
8.3 3
 
1.5%
8.4 5
 
2.5%
8.5 14
6.9%
ValueCountFrequency (%)
23 5
2.5%
22.7 1
 
0.5%
22.5 3
1.5%
22 1
 
0.5%
21.9 1
 
0.5%
21.5 4
2.0%
21 5
2.5%
11.5 1
 
0.5%
10.1 1
 
0.5%
10 3
1.5%

chevaux
Real number (ℝ)

Distinct59
Distinct (%)29.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.39901
Minimum48
Maximum288
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-28T10:57:02.501652image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum48
5-th percentile62
Q170
median95
Q3116
95-th percentile181.4
Maximum288
Range240
Interquartile range (IQR)46

Descriptive statistics

Standard deviation39.631013
Coefficient of variation (CV)0.37961099
Kurtosis2.6470236
Mean104.39901
Median Absolute Deviation (MAD)25
Skewness1.3928436
Sum21193
Variance1570.6172
MonotonicityNot monotonic
2023-04-28T10:57:02.687796image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68 19
 
9.4%
70 11
 
5.4%
116 9
 
4.4%
69 9
 
4.4%
110 8
 
3.9%
95 7
 
3.4%
114 6
 
3.0%
160 6
 
3.0%
101 6
 
3.0%
62 6
 
3.0%
Other values (49) 116
57.1%
ValueCountFrequency (%)
48 1
 
0.5%
52 2
 
1.0%
55 1
 
0.5%
56 2
 
1.0%
58 1
 
0.5%
60 1
 
0.5%
62 6
 
3.0%
64 1
 
0.5%
68 19
9.4%
69 9
4.4%
ValueCountFrequency (%)
288 1
 
0.5%
262 1
 
0.5%
207 3
1.5%
200 1
 
0.5%
184 2
1.0%
182 3
1.5%
176 2
1.0%
175 1
 
0.5%
162 2
1.0%
161 2
1.0%

tour_moteur
Real number (ℝ)

Distinct22
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5127.8325
Minimum4150
Maximum6600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-28T10:57:02.854655image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum4150
5-th percentile4250
Q14800
median5200
Q35500
95-th percentile5990
Maximum6600
Range2450
Interquartile range (IQR)700

Descriptive statistics

Standard deviation478.5252
Coefficient of variation (CV)0.093319195
Kurtosis0.074796639
Mean5127.8325
Median Absolute Deviation (MAD)300
Skewness0.060106102
Sum1040950
Variance228986.37
MonotonicityNot monotonic
2023-04-28T10:57:03.202947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
5500 37
18.2%
4800 35
17.2%
5000 27
13.3%
5200 23
11.3%
5400 13
 
6.4%
6000 9
 
4.4%
4500 7
 
3.4%
5800 7
 
3.4%
5250 7
 
3.4%
5100 5
 
2.5%
Other values (12) 33
16.3%
ValueCountFrequency (%)
4150 5
 
2.5%
4200 5
 
2.5%
4250 3
 
1.5%
4350 4
 
2.0%
4400 3
 
1.5%
4500 7
 
3.4%
4650 1
 
0.5%
4750 4
 
2.0%
4800 35
17.2%
5000 27
13.3%
ValueCountFrequency (%)
6600 2
 
1.0%
6000 9
 
4.4%
5900 3
 
1.5%
5800 7
 
3.4%
5750 1
 
0.5%
5600 1
 
0.5%
5500 37
18.2%
5400 13
 
6.4%
5300 1
 
0.5%
5250 7
 
3.4%
Distinct28
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.9839409
Minimum4.8
Maximum18.09
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-28T10:57:03.354132image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum4.8
5-th percentile6.36
Q17.84
median9.8
Q312.38
95-th percentile14.7
Maximum18.09
Range13.29
Interquartile range (IQR)4.54

Descriptive statistics

Standard deviation2.576031
Coefficient of variation (CV)0.25801746
Kurtosis-0.17598349
Mean9.9839409
Median Absolute Deviation (MAD)2.21
Skewness0.55100854
Sum2026.74
Variance6.6359359
MonotonicityNot monotonic
2023-04-28T10:57:03.511030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
7.59 27
13.3%
12.38 27
13.3%
9.8 22
10.8%
8.71 14
 
6.9%
13.84 13
 
6.4%
9.05 12
 
5.9%
10.23 12
 
5.9%
11.2 8
 
3.9%
9.41 8
 
3.9%
7.84 8
 
3.9%
Other values (18) 52
25.6%
ValueCountFrequency (%)
4.8 1
 
0.5%
5 1
 
0.5%
5.23 1
 
0.5%
6.19 7
 
3.4%
6.36 6
 
3.0%
6.53 1
 
0.5%
6.72 1
 
0.5%
6.92 1
 
0.5%
7.13 1
 
0.5%
7.59 27
13.3%
ValueCountFrequency (%)
18.09 1
 
0.5%
16.8 2
 
1.0%
15.68 3
 
1.5%
14.7 6
 
3.0%
13.84 13
6.4%
13.07 3
 
1.5%
12.38 27
13.3%
11.76 3
 
1.5%
11.2 8
 
3.9%
10.69 4
 
2.0%
Distinct30
Distinct (%)14.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.0578818
Minimum4.36
Maximum14.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-28T10:57:03.682325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum4.36
5-th percentile5.483
Q16.92
median7.84
Q39.41
95-th percentile10.69
Maximum14.7
Range10.34
Interquartile range (IQR)2.49

Descriptive statistics

Standard deviation1.8536995
Coefficient of variation (CV)0.23004799
Kurtosis1.1347336
Mean8.0578818
Median Absolute Deviation (MAD)1.48
Skewness0.81145808
Sum1635.75
Variance3.4362019
MonotonicityNot monotonic
2023-04-28T10:57:03.838477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
9.41 19
 
9.4%
6.19 17
 
8.4%
9.8 17
 
8.4%
7.84 16
 
7.9%
7.35 16
 
7.9%
6.92 14
 
6.9%
8.4 13
 
6.4%
6.36 12
 
5.9%
8.11 10
 
4.9%
7.13 9
 
4.4%
Other values (20) 60
29.6%
ValueCountFrequency (%)
4.36 1
 
0.5%
4.44 1
 
0.5%
4.7 1
 
0.5%
5 2
 
1.0%
5.11 2
 
1.0%
5.47 4
 
2.0%
5.6 3
 
1.5%
5.74 3
 
1.5%
6.03 2
 
1.0%
6.19 17
8.4%
ValueCountFrequency (%)
14.7 2
 
1.0%
13.84 1
 
0.5%
13.07 2
 
1.0%
12.38 2
 
1.0%
11.76 2
 
1.0%
10.69 8
3.9%
10.23 7
 
3.4%
9.8 17
8.4%
9.41 19
9.4%
9.05 3
 
1.5%

prix
Real number (ℝ)

Distinct187
Distinct (%)92.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13347.2
Minimum5151
Maximum45400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-28T10:57:04.019431image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum5151
5-th percentile6229
Q17847
median10345
Q316509
95-th percentile32500.2
Maximum45400
Range40249
Interquartile range (IQR)8662

Descriptive statistics

Standard deviation7995.7399
Coefficient of variation (CV)0.59905745
Kurtosis3.0206407
Mean13347.2
Median Absolute Deviation (MAD)3300
Skewness1.7716934
Sum2709481.7
Variance63931856
MonotonicityNot monotonic
2023-04-28T10:57:04.192923image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8921 2
 
1.0%
18150 2
 
1.0%
7898 2
 
1.0%
8916.5 2
 
1.0%
7775 2
 
1.0%
8845 2
 
1.0%
7295 2
 
1.0%
7609 2
 
1.0%
6692 2
 
1.0%
6229 2
 
1.0%
Other values (177) 183
90.1%
ValueCountFrequency (%)
5151 1
0.5%
5195 1
0.5%
5348 1
0.5%
5389 1
0.5%
5399 1
0.5%
5499 1
0.5%
5572 2
1.0%
6095 1
0.5%
6189 1
0.5%
6229 2
1.0%
ValueCountFrequency (%)
45400 1
0.5%
41315 1
0.5%
40960 1
0.5%
37028 1
0.5%
36880 1
0.5%
36000 1
0.5%
35550 1
0.5%
35056 1
0.5%
34184 1
0.5%
34028 1
0.5%

marque
Categorical

Distinct22
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
toyota
32 
nissan
18 
mazda
17 
mitsubishi
13 
honda
13 
Other values (17)
110 

Length

Max length10
Median length9
Mean length6.2068966
Min length3

Characters and Unicode

Total characters1260
Distinct characters25
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st rowalfa-romeo
2nd rowalfa-romeo
3rd rowalfa-romeo
4th rowaudi
5th rowaudi

Common Values

ValueCountFrequency (%)
toyota 32
15.8%
nissan 18
 
8.9%
mazda 17
 
8.4%
mitsubishi 13
 
6.4%
honda 13
 
6.4%
volkswagen 12
 
5.9%
peugeot 11
 
5.4%
volvo 11
 
5.4%
subaru 10
 
4.9%
dodge 9
 
4.4%
Other values (12) 57
28.1%

Length

2023-04-28T10:57:04.372104image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
toyota 32
15.8%
nissan 18
 
8.9%
mazda 17
 
8.4%
mitsubishi 13
 
6.4%
honda 13
 
6.4%
volkswagen 12
 
5.9%
peugeot 11
 
5.4%
volvo 11
 
5.4%
subaru 10
 
4.9%
dodge 9
 
4.4%
Other values (12) 57
28.1%

Most occurring characters

ValueCountFrequency (%)
a 152
12.1%
o 152
12.1%
t 100
 
7.9%
s 99
 
7.9%
u 80
 
6.3%
i 76
 
6.0%
n 63
 
5.0%
e 60
 
4.8%
d 55
 
4.4%
m 49
 
3.9%
Other values (15) 374
29.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1257
99.8%
Dash Punctuation 3
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 152
12.1%
o 152
12.1%
t 100
 
8.0%
s 99
 
7.9%
u 80
 
6.4%
i 76
 
6.0%
n 63
 
5.0%
e 60
 
4.8%
d 55
 
4.4%
m 49
 
3.9%
Other values (14) 371
29.5%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1257
99.8%
Common 3
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 152
12.1%
o 152
12.1%
t 100
 
8.0%
s 99
 
7.9%
u 80
 
6.4%
i 76
 
6.0%
n 63
 
5.0%
e 60
 
4.8%
d 55
 
4.4%
m 49
 
3.9%
Other values (14) 371
29.5%
Common
ValueCountFrequency (%)
- 3
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1260
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 152
12.1%
o 152
12.1%
t 100
 
7.9%
s 99
 
7.9%
u 80
 
6.3%
i 76
 
6.0%
n 63
 
5.0%
e 60
 
4.8%
d 55
 
4.4%
m 49
 
3.9%
Other values (15) 374
29.7%

modele
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct140
Distinct (%)69.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
504
 
6
corolla
 
6
corona
 
6
dl
 
4
civic
 
3
Other values (135)
178 

Length

Max length22
Median length16
Mean length6.6748768
Min length2

Characters and Unicode

Total characters1355
Distinct characters44
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique101 ?
Unique (%)49.8%

Sample

1st rowgiulia
2nd rowstelvio
3rd rowQuadrifoglio
4th row100ls
5th row100ls

Common Values

ValueCountFrequency (%)
504 6
 
3.0%
corolla 6
 
3.0%
corona 6
 
3.0%
dl 4
 
2.0%
civic 3
 
1.5%
markii 3
 
1.5%
rabbit 3
 
1.5%
g4 3
 
1.5%
outlander 3
 
1.5%
mirageg4 3
 
1.5%
Other values (130) 163
80.3%

Length

2023-04-28T10:57:04.566501image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
504 6
 
3.0%
corona 6
 
3.0%
corolla 6
 
3.0%
dl 4
 
2.0%
mirageg4 3
 
1.5%
626 3
 
1.5%
glcdeluxe 3
 
1.5%
dasher 3
 
1.5%
100ls 3
 
1.5%
outlander 3
 
1.5%
Other values (130) 163
80.3%

Most occurring characters

ValueCountFrequency (%)
c 108
 
8.0%
a 107
 
7.9%
l 103
 
7.6%
r 100
 
7.4%
e 100
 
7.4%
o 93
 
6.9%
i 71
 
5.2%
t 67
 
4.9%
s 54
 
4.0%
0 44
 
3.2%
Other values (34) 508
37.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1128
83.2%
Decimal Number 179
 
13.2%
Close Punctuation 13
 
1.0%
Open Punctuation 13
 
1.0%
Uppercase Letter 12
 
0.9%
Dash Punctuation 10
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c 108
 
9.6%
a 107
 
9.5%
l 103
 
9.1%
r 100
 
8.9%
e 100
 
8.9%
o 93
 
8.2%
i 71
 
6.3%
t 67
 
5.9%
s 54
 
4.8%
u 41
 
3.6%
Other values (15) 284
25.2%
Decimal Number
ValueCountFrequency (%)
0 44
24.6%
4 37
20.7%
1 23
12.8%
2 21
11.7%
5 18
10.1%
9 12
 
6.7%
6 12
 
6.7%
3 10
 
5.6%
7 2
 
1.1%
Uppercase Letter
ValueCountFrequency (%)
M 4
33.3%
D 3
25.0%
V 1
 
8.3%
C 1
 
8.3%
Q 1
 
8.3%
U 1
 
8.3%
X 1
 
8.3%
Close Punctuation
ValueCountFrequency (%)
) 13
100.0%
Open Punctuation
ValueCountFrequency (%)
( 13
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1140
84.1%
Common 215
 
15.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
c 108
 
9.5%
a 107
 
9.4%
l 103
 
9.0%
r 100
 
8.8%
e 100
 
8.8%
o 93
 
8.2%
i 71
 
6.2%
t 67
 
5.9%
s 54
 
4.7%
u 41
 
3.6%
Other values (22) 296
26.0%
Common
ValueCountFrequency (%)
0 44
20.5%
4 37
17.2%
1 23
10.7%
2 21
9.8%
5 18
8.4%
) 13
 
6.0%
( 13
 
6.0%
9 12
 
5.6%
6 12
 
5.6%
3 10
 
4.7%
Other values (2) 12
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1355
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c 108
 
8.0%
a 107
 
7.9%
l 103
 
7.6%
r 100
 
7.4%
e 100
 
7.4%
o 93
 
6.9%
i 71
 
5.2%
t 67
 
4.9%
s 54
 
4.0%
0 44
 
3.2%
Other values (34) 508
37.5%

Interactions

2023-04-28T10:56:52.588385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T10:56:24.454789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T10:56:26.515317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T10:56:28.464992image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T10:56:30.492389image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T10:56:32.634194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T10:56:34.645250image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T10:56:36.584415image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T10:56:38.503791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T10:56:40.678479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T10:56:42.597071image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T10:56:44.632855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T10:56:46.534050image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T10:56:48.449792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T10:56:50.583453image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T10:56:52.729205image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T10:56:24.586607image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T10:56:26.639171image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T10:56:28.600619image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T10:56:30.622056image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-04-28T10:56:38.346428image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T10:56:40.532001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T10:56:42.458378image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T10:56:44.489008image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T10:56:46.396390image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T10:56:48.311077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T10:56:50.438692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-04-28T10:56:52.438442image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-04-28T10:57:04.753451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
niveau_risque_assuranceempattement(cm)longueur_voiture(cm)largeur_voiture(cm)hauteur_voiture(cm)poids_vehicule(kg)taille_moteurtaux_alésage(cm)course_piston(cm)taux_compressionchevauxtour_moteurconsommation_ville(L/100km)consommation_autoroute(L/100km)prixcarburantturbonombre_portestype_vehiculeroues_motricesemplacement_moteurtype_moteurnombre_cylindressysteme_carburantmarque
niveau_risque_assurance1.000-0.537-0.394-0.250-0.527-0.258-0.175-0.173-0.0160.029-0.0060.2830.019-0.053-0.1430.2190.1810.6820.3340.2660.2710.2210.1600.2690.442
empattement(cm)-0.5371.0000.9120.8110.6360.7660.6460.5510.221-0.1310.501-0.3170.4930.5380.6820.3400.3090.4440.3320.4170.5680.3550.3160.2260.507
longueur_voiture(cm)-0.3940.9121.0000.8870.5290.8910.7800.6580.176-0.1940.657-0.2770.6690.6970.8040.1040.2060.3620.2390.4090.0000.3150.3560.3260.499
largeur_voiture(cm)-0.2500.8110.8871.0000.3530.8650.7700.6300.230-0.1490.686-0.2070.6880.7010.8110.2320.3240.2330.1040.4030.1360.3710.5680.2550.527
hauteur_voiture(cm)-0.5270.6360.5290.3531.0000.3460.1980.222-0.0240.0040.008-0.3000.0640.1290.2410.2780.2420.5630.4860.3750.1970.3800.3480.2880.479
poids_vehicule(kg)-0.2580.7660.8910.8650.3461.0000.8770.7200.150-0.2160.806-0.2450.8110.8320.9090.3030.3740.2760.2300.4540.0970.3250.4820.2900.492
taille_moteur-0.1750.6460.7800.7700.1980.8771.0000.7160.284-0.2330.815-0.2810.7290.7190.8260.1550.2680.2080.2010.4670.6180.5270.6420.3310.531
taux_alésage(cm)-0.1730.5510.6580.6300.2220.7200.7161.000-0.065-0.1700.655-0.2940.6330.6320.6720.1510.3390.1480.1370.4420.3180.4140.2560.3410.517
course_piston(cm)-0.0160.2210.1760.230-0.0240.1500.284-0.0651.000-0.0660.117-0.0870.0100.0140.0900.3800.2710.1200.1650.3560.6180.4040.2480.3080.623
taux_compression0.029-0.131-0.194-0.1490.004-0.216-0.233-0.170-0.0661.000-0.353-0.015-0.477-0.443-0.1720.9930.5530.1870.0470.1110.0000.3360.5210.5180.491
chevaux-0.0060.5010.6570.6860.0080.8060.8150.6550.117-0.3531.0000.1070.9110.8840.8540.2210.3410.1640.1880.4000.8430.5180.5640.3170.458
tour_moteur0.283-0.317-0.277-0.207-0.300-0.245-0.281-0.294-0.087-0.0150.1071.0000.1220.050-0.0770.5940.3100.2390.0710.2460.4470.3620.2820.3630.472
consommation_ville(L/100km)0.0190.4930.6690.6880.0640.8110.7290.6330.010-0.4770.9110.1221.0000.9680.8280.3120.1860.1190.0750.3860.3630.3380.4970.3160.403
consommation_autoroute(L/100km)-0.0530.5380.6970.7010.1290.8320.7190.6320.014-0.4430.8840.0500.9681.0000.8220.3610.2980.1700.1790.4340.2310.3450.5140.3360.380
prix-0.1430.6820.8040.8110.2410.9090.8260.6720.090-0.1720.854-0.0770.8280.8221.0000.3290.4040.0000.2290.4440.4500.2850.4300.2870.377
carburant0.2190.3400.1040.2320.2780.3030.1550.1510.3800.9930.2210.5940.3120.3610.3291.0000.3730.1610.1740.0850.0000.2470.1540.9850.368
turbo0.1810.3090.2060.3240.2420.3740.2680.3390.2710.5530.3410.3100.1860.2980.4040.3731.0000.0000.0000.1140.0000.1460.1960.6090.410
nombre_portes0.6820.4440.3620.2330.5630.2760.2080.1480.1200.1870.1640.2390.1190.1700.0000.1610.0001.0000.7390.0510.0680.2020.1350.2460.305
type_vehicule0.3340.3320.2390.1040.4860.2300.2010.1370.1650.0470.1880.0710.0750.1790.2290.1740.0000.7391.0000.2120.4380.1450.0670.1440.321
roues_motrices0.2660.4170.4090.4030.3750.4540.4670.4420.3560.1110.4000.2460.3860.4340.4440.0850.1140.0510.2121.0000.1230.4420.3340.3850.621
emplacement_moteur0.2710.5680.0000.1360.1970.0970.6180.3180.6180.0000.8430.4470.3630.2310.4500.0000.0000.0680.4380.1231.0000.4360.2870.0000.702
type_moteur0.2210.3550.3150.3710.3800.3250.5270.4140.4040.3360.5180.3620.3380.3450.2850.2470.1460.2020.1450.4420.4361.0000.5460.3750.626
nombre_cylindres0.1600.3160.3560.5680.3480.4820.6420.2560.2480.5210.5640.2820.4970.5140.4300.1540.1960.1350.0670.3340.2870.5461.0000.3730.543
systeme_carburant0.2690.2260.3260.2550.2880.2900.3310.3410.3080.5180.3170.3630.3160.3360.2870.9850.6090.2460.1440.3850.0000.3750.3731.0000.508
marque0.4420.5070.4990.5270.4790.4920.5310.5170.6230.4910.4580.4720.4030.3800.3770.3680.4100.3050.3210.6210.7020.6260.5430.5081.000

Missing values

2023-04-28T10:56:54.810227image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-28T10:56:55.330131image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

niveau_risque_assurancecarburantturbonombre_portestype_vehiculeroues_motricesemplacement_moteurempattement(cm)longueur_voiture(cm)largeur_voiture(cm)hauteur_voiture(cm)poids_vehicule(kg)type_moteurnombre_cylindrestaille_moteursysteme_carburanttaux_alésage(cm)course_piston(cm)taux_compressionchevauxtour_moteurconsommation_ville(L/100km)consommation_autoroute(L/100km)prixmarquemodele
03gasstdtwodécapotablepropulsionavant225.04428.75162.81123.951155.75dohcfour130mpfi8.816.819.0111500011.208.7113495.000alfa-romeogiulia
13gasstdtwodécapotablepropulsionavant225.04428.75162.81123.951155.75dohcfour130mpfi8.816.819.0111500011.208.7116500.000alfa-romeostelvio
21gasstdtwohayonpropulsionavant240.03434.85166.37133.101280.49ohcvsix152mpfi6.818.819.0154500012.389.0516500.000alfa-romeoQuadrifoglio
32gasstdfourberlinetractionavant253.49448.56168.15137.921060.05ohcfour109mpfi8.108.6410.010255009.807.8413950.000audi100ls
42gasstdfourberlinequatre_roues_motricesavant252.48448.56168.66137.921280.94ohcfive136mpfi8.108.648.0115550013.0710.6917450.000audi100ls
52gasstdtwoberlinetractionavant253.49450.34168.40134.871137.16ohcfive136mpfi8.108.648.5110550012.389.4115250.000audifox
61gasstdfourberlinetractionavant268.73489.46181.36141.481290.02ohcfive136mpfi8.108.648.5110550012.389.4117710.000audi100ls
71gasstdfourbreaktractionavant268.73489.46181.36141.481339.91ohcfive136mpfi8.108.648.5110550012.389.4118920.000audi5000
81gasturbofourberlinetractionavant268.73489.46181.36141.991399.79ohcfive131mpfi7.958.648.3140550013.8411.7623875.000audi4000
90gasturbotwohayonquatre_roues_motricesavant252.73452.63172.47132.081384.82ohcfive131mpfi7.958.647.0160550014.7010.6917859.167audi5000s(diesel)
niveau_risque_assurancecarburantturbonombre_portestype_vehiculeroues_motricesemplacement_moteurempattement(cm)longueur_voiture(cm)largeur_voiture(cm)hauteur_voiture(cm)poids_vehicule(kg)type_moteurnombre_cylindrestaille_moteursysteme_carburanttaux_alésage(cm)course_piston(cm)taux_compressionchevauxtour_moteurconsommation_ville(L/100km)consommation_autoroute(L/100km)prixmarquemodele
193-1gasstdfourbreakpropulsionavant264.92479.55170.69146.051376.20ohcfour141mpfi9.608.009.5114540010.238.4013415.0volvo144ea
194-2gasstdfourberlinepropulsionavant264.92479.55170.69142.751331.29ohcfour141mpfi9.608.009.511454009.808.4015985.0volvo244dl
195-1gasstdfourbreakpropulsionavant264.92479.55170.69146.051379.83ohcfour141mpfi9.608.009.511454009.808.4016515.0volvo245
196-2gasturbofourberlinepropulsionavant264.92479.55170.69142.751381.19ohcfour130mpfi9.198.007.5162510013.8410.6918420.0volvo264gl
197-1gasturbofourbreakpropulsionavant264.92479.55170.69146.051431.99ohcfour130mpfi9.198.007.5162510013.8410.6918950.0volvodiesel
198-1gasstdfourberlinepropulsionavant277.11479.55175.01140.971339.00ohcfour141mpfi9.608.009.5114540010.238.4016845.0volvo145e(sw)
199-1gasturbofourberlinepropulsionavant277.11479.55174.75140.971383.00ohcfour141mpfi9.608.008.7160530012.389.4119045.0volvo144ea
200-1gasstdfourberlinepropulsionavant277.11479.55175.01140.971366.22ohcvsix173mpfi9.097.298.8134550013.0710.2321485.0volvo244dl
201-1dieselturbofourberlinepropulsionavant277.11479.55175.01140.971459.21ohcsix145idi7.658.6423.010648009.058.7122470.0volvo246
202-1gasturbofourberlinepropulsionavant277.11479.55175.01140.971388.90ohcfour141mpfi9.608.009.5114540012.389.4122625.0volvo264gl